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Add scVelo RNA velocity analysis workflow and IQ-TREE reference documentation
- Introduced a comprehensive RNA velocity analysis pipeline using scVelo, including data loading, preprocessing, velocity estimation, and visualization. - Added a script for running RNA velocity analysis with customizable parameters and output options. - Created detailed documentation for IQ-TREE 2 phylogenetic inference, covering command syntax, model selection, bootstrapping methods, and output interpretation. - Included references for velocity models and their mathematical framework, along with a comparison of different models. - Enhanced the scVelo skill documentation with installation instructions, use cases, and best practices for RNA velocity analysis.
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huangkuanlin
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scientific-skills/gtex-database/SKILL.md
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scientific-skills/gtex-database/SKILL.md
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---
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name: gtex-database
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description: Query GTEx (Genotype-Tissue Expression) portal for tissue-specific gene expression, eQTLs (expression quantitative trait loci), and sQTLs. Essential for linking GWAS variants to gene regulation, understanding tissue-specific expression, and interpreting non-coding variant effects.
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license: CC-BY-4.0
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metadata:
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skill-author: Kuan-lin Huang
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---
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# GTEx Database
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## Overview
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The Genotype-Tissue Expression (GTEx) project provides a comprehensive resource for studying tissue-specific gene expression and genetic regulation across 54 non-diseased human tissues from nearly 1,000 individuals. GTEx v10 (the latest release) enables researchers to understand how genetic variants regulate gene expression (eQTLs) and splicing (sQTLs) in a tissue-specific manner, which is critical for interpreting GWAS loci and identifying regulatory mechanisms.
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**Key resources:**
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- GTEx Portal: https://gtexportal.org/
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- GTEx API v2: https://gtexportal.org/api/v2/
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- Data downloads: https://gtexportal.org/home/downloads/adult-gtex/
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- Documentation: https://gtexportal.org/home/documentationPage
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## When to Use This Skill
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Use GTEx when:
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- **GWAS locus interpretation**: Identifying which gene a non-coding GWAS variant regulates via eQTLs
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- **Tissue-specific expression**: Comparing gene expression levels across 54 human tissues
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- **eQTL colocalization**: Testing if a GWAS signal and an eQTL signal share the same causal variant
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- **Multi-tissue eQTL analysis**: Finding variants that regulate expression in multiple tissues
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- **Splicing QTLs (sQTLs)**: Identifying variants that affect splicing ratios
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- **Tissue specificity analysis**: Determining which tissues express a gene of interest
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- **Gene expression exploration**: Retrieving normalized expression levels (TPM) per tissue
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## Core Capabilities
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### 1. GTEx REST API v2
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Base URL: `https://gtexportal.org/api/v2/`
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The API returns JSON and does not require authentication. All endpoints support pagination.
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```python
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import requests
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BASE_URL = "https://gtexportal.org/api/v2"
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def gtex_get(endpoint, params=None):
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"""Make a GET request to the GTEx API."""
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url = f"{BASE_URL}/{endpoint}"
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response = requests.get(url, params=params, headers={"Accept": "application/json"})
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response.raise_for_status()
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return response.json()
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```
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### 2. Gene Expression by Tissue
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```python
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import requests
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import pandas as pd
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def get_gene_expression_by_tissue(gene_id_or_symbol, dataset_id="gtex_v10"):
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"""Get median gene expression across all tissues."""
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url = "https://gtexportal.org/api/v2/expression/medianGeneExpression"
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params = {
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"gencodeId": gene_id_or_symbol,
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"datasetId": dataset_id,
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"itemsPerPage": 100
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}
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response = requests.get(url, params=params)
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data = response.json()
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records = data.get("data", [])
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df = pd.DataFrame(records)
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if not df.empty:
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df = df[["tissueSiteDetailId", "tissueSiteDetail", "median", "unit"]].sort_values(
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"median", ascending=False
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)
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return df
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# Example: get expression of APOE across tissues
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df = get_gene_expression_by_tissue("ENSG00000130203.10") # APOE GENCODE ID
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# Or use gene symbol (some endpoints accept both)
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print(df.head(10))
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# Output: tissue name, median TPM, sorted by highest expression
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```
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### 3. eQTL Lookup
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```python
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import requests
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import pandas as pd
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def query_eqtl(gene_id, tissue_id=None, dataset_id="gtex_v10"):
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"""Query significant eQTLs for a gene, optionally filtered by tissue."""
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url = "https://gtexportal.org/api/v2/association/singleTissueEqtl"
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params = {
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"gencodeId": gene_id,
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"datasetId": dataset_id,
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"itemsPerPage": 250
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}
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if tissue_id:
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params["tissueSiteDetailId"] = tissue_id
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all_results = []
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page = 0
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while True:
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params["page"] = page
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response = requests.get(url, params=params)
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data = response.json()
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results = data.get("data", [])
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if not results:
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break
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all_results.extend(results)
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if len(results) < params["itemsPerPage"]:
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break
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page += 1
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df = pd.DataFrame(all_results)
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if not df.empty:
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df = df.sort_values("pval", ascending=True)
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return df
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# Example: Find eQTLs for PCSK9
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df = query_eqtl("ENSG00000169174.14")
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print(df[["snpId", "tissueSiteDetailId", "slope", "pval", "gencodeId"]].head(20))
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```
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### 4. Single-Tissue eQTL by Variant
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```python
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import requests
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def query_variant_eqtl(variant_id, tissue_id=None, dataset_id="gtex_v10"):
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"""Get all eQTL associations for a specific variant."""
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url = "https://gtexportal.org/api/v2/association/singleTissueEqtl"
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params = {
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"variantId": variant_id, # e.g., "chr1_55516888_G_GA_b38"
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"datasetId": dataset_id,
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"itemsPerPage": 250
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}
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if tissue_id:
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params["tissueSiteDetailId"] = tissue_id
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response = requests.get(url, params=params)
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return response.json()
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# GTEx variant ID format: chr{chrom}_{pos}_{ref}_{alt}_b38
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# Example: "chr17_43094692_G_A_b38"
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```
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### 5. Multi-Tissue eQTL (eGenes)
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```python
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import requests
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def get_egenes(tissue_id, dataset_id="gtex_v10"):
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"""Get all eGenes (genes with at least one significant eQTL) in a tissue."""
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url = "https://gtexportal.org/api/v2/association/egene"
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params = {
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"tissueSiteDetailId": tissue_id,
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"datasetId": dataset_id,
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"itemsPerPage": 500
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}
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all_egenes = []
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page = 0
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while True:
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params["page"] = page
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response = requests.get(url, params=params)
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data = response.json()
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batch = data.get("data", [])
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if not batch:
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break
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all_egenes.extend(batch)
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if len(batch) < params["itemsPerPage"]:
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break
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page += 1
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return all_egenes
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# Example: all eGenes in whole blood
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egenes = get_egenes("Whole_Blood")
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print(f"Found {len(egenes)} eGenes in Whole Blood")
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```
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### 6. Tissue List
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```python
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import requests
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def get_tissues(dataset_id="gtex_v10"):
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"""Get all available tissues with metadata."""
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url = "https://gtexportal.org/api/v2/dataset/tissueSiteDetail"
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params = {"datasetId": dataset_id, "itemsPerPage": 100}
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response = requests.get(url, params=params)
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return response.json()["data"]
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tissues = get_tissues()
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# Key fields: tissueSiteDetailId, tissueSiteDetail, colorHex, samplingSite
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# Common tissue IDs:
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# Whole_Blood, Brain_Cortex, Liver, Kidney_Cortex, Heart_Left_Ventricle,
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# Lung, Muscle_Skeletal, Adipose_Subcutaneous, Colon_Transverse, ...
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```
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### 7. sQTL (Splicing QTLs)
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```python
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import requests
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def query_sqtl(gene_id, tissue_id=None, dataset_id="gtex_v10"):
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"""Query significant sQTLs for a gene."""
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url = "https://gtexportal.org/api/v2/association/singleTissueSqtl"
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params = {
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"gencodeId": gene_id,
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"datasetId": dataset_id,
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"itemsPerPage": 250
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}
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if tissue_id:
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params["tissueSiteDetailId"] = tissue_id
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response = requests.get(url, params=params)
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return response.json()
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```
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## Query Workflows
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### Workflow 1: Interpreting a GWAS Variant via eQTLs
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1. **Identify the GWAS variant** (rs ID or chromosome position)
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2. **Convert to GTEx variant ID format** (`chr{chrom}_{pos}_{ref}_{alt}_b38`)
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3. **Query all eQTL associations** for that variant across tissues
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4. **Check effect direction**: is the GWAS risk allele the same as the eQTL effect allele?
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5. **Prioritize tissues**: select tissues biologically relevant to the disease
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6. **Consider colocalization** using `coloc` (R package) with full summary statistics
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```python
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import requests, pandas as pd
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def interpret_gwas_variant(variant_id, dataset_id="gtex_v10"):
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"""Find all genes regulated by a GWAS variant."""
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url = "https://gtexportal.org/api/v2/association/singleTissueEqtl"
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params = {"variantId": variant_id, "datasetId": dataset_id, "itemsPerPage": 500}
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response = requests.get(url, params=params)
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data = response.json()
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df = pd.DataFrame(data.get("data", []))
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if df.empty:
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return df
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return df[["geneSymbol", "tissueSiteDetailId", "slope", "pval", "maf"]].sort_values("pval")
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# Example
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results = interpret_gwas_variant("chr1_154453788_A_T_b38")
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print(results.groupby("geneSymbol")["tissueSiteDetailId"].count().sort_values(ascending=False))
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```
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### Workflow 2: Gene Expression Atlas
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1. Get median expression for a gene across all tissues
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2. Identify the primary expression site(s)
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3. Compare with disease-relevant tissues
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4. Download raw data for statistical comparisons
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### Workflow 3: Tissue-Specific eQTL Analysis
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1. Select tissues relevant to your disease
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2. Query all eGenes in that tissue
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3. Cross-reference with GWAS-significant loci
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4. Identify co-localized signals
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## Key API Endpoints
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| Endpoint | Description |
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|----------|-------------|
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| `/expression/medianGeneExpression` | Median TPM by tissue for a gene |
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| `/expression/geneExpression` | Full distribution of expression per tissue |
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| `/association/singleTissueEqtl` | Significant eQTL associations |
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| `/association/singleTissueSqtl` | Significant sQTL associations |
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| `/association/egene` | eGenes in a tissue |
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| `/dataset/tissueSiteDetail` | Available tissues with metadata |
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| `/reference/gene` | Gene metadata (GENCODE IDs, coordinates) |
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| `/variant/variantPage` | Variant lookup by rsID or position |
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## Datasets Available
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| ID | Description |
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|----|-------------|
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| `gtex_v10` | GTEx v10 (current; ~960 donors, 54 tissues) |
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| `gtex_v8` | GTEx v8 (838 donors, 49 tissues) — older but widely cited |
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## Best Practices
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- **Use GENCODE IDs** (e.g., `ENSG00000130203.10`) for gene queries; the `.version` suffix matters for some endpoints
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- **GTEx variant IDs** use the format `chr{chrom}_{pos}_{ref}_{alt}_b38` (GRCh38) — different from rs IDs
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- **Handle pagination**: Large queries (e.g., all eGenes) require iterating through pages
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- **Tissue nomenclature**: Use `tissueSiteDetailId` (e.g., `Whole_Blood`) not display names for API calls
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- **FDR correction**: GTEx uses FDR < 0.05 (q-value) as the significance threshold for eQTLs
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- **Effect alleles**: The `slope` field is the effect of the alternative allele; positive = higher expression with alt allele
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## Data Downloads (for large-scale analysis)
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For genome-wide analyses, download full summary statistics rather than using the API:
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```bash
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# All significant eQTLs (v10)
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wget https://storage.googleapis.com/adult-gtex/bulk-qtl/v10/single-tissue-cis-qtl/GTEx_Analysis_v10_eQTL.tar
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# Normalized expression matrices
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wget https://storage.googleapis.com/adult-gtex/bulk-gex/v10/rna-seq/GTEx_Analysis_v10_RNASeQCv2.4.2_gene_reads.gct.gz
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```
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## Additional Resources
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- **GTEx Portal**: https://gtexportal.org/
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- **API documentation**: https://gtexportal.org/api/v2/
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- **Data downloads**: https://gtexportal.org/home/downloads/adult-gtex/
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- **GitHub**: https://github.com/broadinstitute/gtex-pipeline
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- **Citation**: GTEx Consortium (2020) Science. PMID: 32913098
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